A fusion model for multi-source detect data of section average velocity based on BP network

As section average velocity that is one of the most important traffic flow parameters has a wide range of sources of data, different sources of data vary in standards, advantages and disadvantages. Single-detect equipment can't meet the needs of multi-purpose and different environments. What�...

Full description

Saved in:
Bibliographic Details
Main Authors: Dong Honghui, Wu Mingchao, Jin Maojing, Zhang Pengfei, Zhang Yu, Jia Limin, Qin Yong
Format: Conference Proceeding
Language:English
Subjects:
B-P neural network
Biological neural networks
Data fusion
Data integration
Data models
Detectors
Freeway
Microwave theory and techniques
Section average velocity
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As section average velocity that is one of the most important traffic flow parameters has a wide range of sources of data, different sources of data vary in standards, advantages and disadvantages. Single-detect equipment can't meet the needs of multi-purpose and different environments. What's more, under certain conditions, the detector performance is defective, and it can't get rich and high-quality section average velocity information. The paper will try to use B-P neural network to do date fusion, to get more realistic traffic flow speed information, to provide a basis for traffic management, control, and induction measures. Taking Beijing as the research background, the expressway section average velocity of multi-source data is adopted to do data fusion in the final section of the study.
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2013.6561300